Dried Blood Spot Sampling of Landlocked Arctic Char (<i>Salvelinus alpinus</i>) for Estimating Mercury Exposure and Stable Carbon Isotope Fingerprinting of Essential Amino Acids
Bibliographic record
Abstract
Abstract Dried blood spots (DBS), created by applying and drying a whole blood sample onto filter paper, provide a simple and minimally invasive procedure for collecting, transporting, and storing blood. Because DBS are ideal for use in field and resource-limited settings, we aimed to develop a simple and accurate DBS-based approach for assessing mercury (Hg) exposure and dietary carbon sources for landlocked Arctic char, a sentinel fish species in the Arctic. We collected liquid whole blood (from the caudal vein), muscle, liver, and brains of Arctic char (n = 36) from 8 lakes spanning a Hg gradient in the Canadian High Arctic. We measured total Hg concentrations ([THg]) of field-prepared DBS and Arctic char tissues. Across a considerable range, [THg] of DBS (0.04–3.38 μg/g wet wt) were highly correlated with [THg] of all tissues (r2 range = 0.928–0.996). We also analyzed the compound-specific carbon isotope ratios (expressed as δ13C values) of essential amino acids (EAAs) isolated from DBS, liquid whole blood, and muscle. The δ13C values of 5 EAAs (δ13CEAAs; isoleucine [Ile], leucine [Leu], phenylalanine [Phe], valine [Val], and threonine [Thr]) from DBS were highly correlated with δ13CEAAs of liquid whole blood (r2 range = 0.693–0.895) and muscle (r2 range = 0.642–0.881). The patterns of δ13CEAAs of landlocked Arctic char were remarkably consistent across sample types and indicate that EAAs are most likely of algal origin. Because a small volume of blood (~50 µL) dried on filter paper can be used to determine Hg exposure levels of various tissues and to fingerprint carbon sources, DBS sampling may decrease the burdens of research and may be developed as a nonlethal sampling technique. Environ Toxicol Chem 2020;39:893–903. © 2020 SETAC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".